Overview

Dataset statistics

Number of variables35
Number of observations86971
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.2 MiB
Average record size in memory280.0 B

Variable types

Numeric13
Text10
Categorical9
DateTime3

Alerts

address_id is highly overall correlated with customer_id and 1 other fieldsHigh correlation
amount is highly overall correlated with rental_rateHigh correlation
customer_id is highly overall correlated with address_id and 1 other fieldsHigh correlation
film_id is highly overall correlated with inventory_idHigh correlation
inventory_id is highly overall correlated with film_idHigh correlation
payment_id is highly overall correlated with address_id and 1 other fieldsHigh correlation
rental_rate is highly overall correlated with amountHigh correlation
staff_fn is highly overall correlated with staff_id and 2 other fieldsHigh correlation
staff_id is highly overall correlated with staff_fn and 2 other fieldsHigh correlation
staff_ln is highly overall correlated with staff_fn and 2 other fieldsHigh correlation
store_id is highly overall correlated with staff_fn and 2 other fieldsHigh correlation
active is highly imbalanced (82.7%)Imbalance

Reproduction

Analysis started2026-01-14 07:00:40.176811
Analysis finished2026-01-14 07:00:54.499272
Duration14.32 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

film_id
Real number (ℝ)

High correlation 

Distinct955
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean502.29349
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size679.6 KiB
2026-01-14T10:00:54.549262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile48
Q1256
median496
Q3752
95-th percentile952
Maximum1000
Range999
Interquartile range (IQR)496

Descriptive statistics

Standard deviation288.20778
Coefficient of variation (CV)0.57378364
Kurtosis-1.1859664
Mean502.29349
Median Absolute Deviation (MAD)247
Skewness-0.011599022
Sum43684967
Variance83063.727
MonotonicityNot monotonic
2026-01-14T10:00:54.636453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
489279
 
0.3%
880275
 
0.3%
249273
 
0.3%
649264
 
0.3%
892252
 
0.3%
764252
 
0.3%
418248
 
0.3%
369248
 
0.3%
301243
 
0.3%
414240
 
0.3%
Other values (945)84397
97.0%
ValueCountFrequency (%)
1220
0.3%
224
 
< 0.1%
360
 
0.1%
4110
0.1%
555
 
0.1%
6147
0.2%
775
 
0.1%
872
 
0.1%
9108
0.1%
10184
0.2%
ValueCountFrequency (%)
100093
0.1%
99985
0.1%
99848
 
0.1%
99730
 
< 0.1%
99635
 
< 0.1%
99523
 
< 0.1%
99478
0.1%
993180
0.2%
99256
 
0.1%
99164
 
0.1%

title
Text

Distinct955
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size679.6 KiB
2026-01-14T10:00:54.786148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length27
Median length22
Mean length14.315887
Min length8

Characters and Unicode

Total characters1245067
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBLANKET BEVERLY
2nd rowBLANKET BEVERLY
3rd rowBLANKET BEVERLY
4th rowBLANKET BEVERLY
5th rowFREAKY POCUS
ValueCountFrequency (%)
heartbreakers659
 
0.4%
hellfighters642
 
0.4%
armageddon636
 
0.4%
boondock631
 
0.4%
apollo611
 
0.4%
polish592
 
0.3%
desire563
 
0.3%
shakespeare562
 
0.3%
instinct558
 
0.3%
love548
 
0.3%
Other values (974)167940
96.5%
2026-01-14T10:00:54.949263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E123364
 
9.9%
A105035
 
8.4%
R91026
 
7.3%
O87779
 
7.1%
86971
 
7.0%
N82351
 
6.6%
I82017
 
6.6%
S77792
 
6.2%
T72061
 
5.8%
L59662
 
4.8%
Other values (17)377009
30.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1158096
93.0%
Space Separator86971
 
7.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E123364
 
10.7%
A105035
 
9.1%
R91026
 
7.9%
O87779
 
7.6%
N82351
 
7.1%
I82017
 
7.1%
S77792
 
6.7%
T72061
 
6.2%
L59662
 
5.2%
C47369
 
4.1%
Other values (16)329640
28.5%
Space Separator
ValueCountFrequency (%)
86971
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1158096
93.0%
Common86971
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E123364
 
10.7%
A105035
 
9.1%
R91026
 
7.9%
O87779
 
7.6%
N82351
 
7.1%
I82017
 
7.1%
S77792
 
6.7%
T72061
 
6.2%
L59662
 
5.2%
C47369
 
4.1%
Other values (16)329640
28.5%
Common
ValueCountFrequency (%)
86971
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1245067
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E123364
 
9.9%
A105035
 
8.4%
R91026
 
7.3%
O87779
 
7.1%
86971
 
7.0%
N82351
 
6.6%
I82017
 
6.6%
S77792
 
6.2%
T72061
 
5.8%
L59662
 
4.8%
Other values (17)377009
30.3%
Distinct955
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size679.6 KiB
2026-01-14T10:00:55.045502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length130
Median length115
Mean length94.315335
Min length70

Characters and Unicode

Total characters8202699
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA Emotional Documentary of a Student And a Girl who must Build a Boat in Nigeria
2nd rowA Emotional Documentary of a Student And a Girl who must Build a Boat in Nigeria
3rd rowA Emotional Documentary of a Student And a Girl who must Build a Boat in Nigeria
4th rowA Emotional Documentary of a Student And a Girl who must Build a Boat in Nigeria
5th rowA Fast-Paced Documentary of a Pastry Chef And a Crocodile who must Chase a Squirrel in The Gulf of Mexico
ValueCountFrequency (%)
a384073
24.0%
of89982
 
5.6%
and86971
 
5.4%
who86971
 
5.4%
must86971
 
5.4%
in86971
 
5.4%
the16699
 
1.0%
mad15668
 
1.0%
shark10557
 
0.7%
boat10228
 
0.6%
Other values (139)723265
45.3%
2026-01-14T10:00:55.198390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1511385
18.4%
a718977
 
8.8%
n565473
 
6.9%
o502463
 
6.1%
e490408
 
6.0%
t468027
 
5.7%
i416232
 
5.1%
r335873
 
4.1%
s295545
 
3.6%
A282804
 
3.4%
Other values (42)2615512
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5657078
69.0%
Space Separator1511385
 
18.4%
Uppercase Letter1017316
 
12.4%
Dash Punctuation16920
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a718977
12.7%
n565473
10.0%
o502463
 
8.9%
e490408
 
8.7%
t468027
 
8.3%
i416232
 
7.4%
r335873
 
5.9%
s295545
 
5.2%
u269218
 
4.8%
h206132
 
3.6%
Other values (16)1388730
24.5%
Uppercase Letter
ValueCountFrequency (%)
A282804
27.8%
S101761
 
10.0%
C84099
 
8.3%
M68893
 
6.8%
D60784
 
6.0%
B58596
 
5.8%
T56809
 
5.6%
F56157
 
5.5%
P46203
 
4.5%
W32559
 
3.2%
Other values (14)168651
16.6%
Space Separator
ValueCountFrequency (%)
1511385
100.0%
Dash Punctuation
ValueCountFrequency (%)
-16920
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6674394
81.4%
Common1528305
 
18.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a718977
 
10.8%
n565473
 
8.5%
o502463
 
7.5%
e490408
 
7.3%
t468027
 
7.0%
i416232
 
6.2%
r335873
 
5.0%
s295545
 
4.4%
A282804
 
4.2%
u269218
 
4.0%
Other values (40)2329374
34.9%
Common
ValueCountFrequency (%)
1511385
98.9%
-16920
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8202699
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1511385
18.4%
a718977
 
8.8%
n565473
 
6.9%
o502463
 
6.1%
e490408
 
6.0%
t468027
 
5.7%
i416232
 
5.1%
r335873
 
4.1%
s295545
 
3.6%
A282804
 
3.4%
Other values (42)2615512
31.9%

rental_duration
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size679.6 KiB
6
19042 
4
18450 
3
17938 
5
17358 
7
14183 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters86971
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7
2nd row7
3rd row7
4th row7
5th row7

Common Values

ValueCountFrequency (%)
619042
21.9%
418450
21.2%
317938
20.6%
517358
20.0%
714183
16.3%

Length

2026-01-14T10:00:55.242535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T10:00:55.318317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
619042
21.9%
418450
21.2%
317938
20.6%
517358
20.0%
714183
16.3%

Most occurring characters

ValueCountFrequency (%)
619042
21.9%
418450
21.2%
317938
20.6%
517358
20.0%
714183
16.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number86971
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
619042
21.9%
418450
21.2%
317938
20.6%
517358
20.0%
714183
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common86971
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
619042
21.9%
418450
21.2%
317938
20.6%
517358
20.0%
714183
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII86971
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
619042
21.9%
418450
21.2%
317938
20.6%
517358
20.0%
714183
16.3%

rental_rate
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size679.6 KiB
0.99
30746 
2.99
28626 
4.99
27599 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters347884
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.99
2nd row2.99
3rd row2.99
4th row2.99
5th row2.99

Common Values

ValueCountFrequency (%)
0.9930746
35.4%
2.9928626
32.9%
4.9927599
31.7%

Length

2026-01-14T10:00:55.410874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T10:00:55.476076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.9930746
35.4%
2.9928626
32.9%
4.9927599
31.7%

Most occurring characters

ValueCountFrequency (%)
9173942
50.0%
.86971
25.0%
030746
 
8.8%
228626
 
8.2%
427599
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number260913
75.0%
Other Punctuation86971
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9173942
66.7%
030746
 
11.8%
228626
 
11.0%
427599
 
10.6%
Other Punctuation
ValueCountFrequency (%)
.86971
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common347884
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9173942
50.0%
.86971
25.0%
030746
 
8.8%
228626
 
8.2%
427599
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII347884
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9173942
50.0%
.86971
25.0%
030746
 
8.8%
228626
 
8.2%
427599
 
7.9%

length
Real number (ℝ)

Distinct140
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.15425
Minimum46
Maximum185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size679.6 KiB
2026-01-14T10:00:55.514224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile52
Q180
median114
Q3150
95-th percentile178
Maximum185
Range139
Interquartile range (IQR)70

Descriptive statistics

Standard deviation40.508461
Coefficient of variation (CV)0.35177566
Kurtosis-1.1983035
Mean115.15425
Median Absolute Deviation (MAD)35
Skewness0.026121438
Sum10015080
Variance1640.9354
MonotonicityNot monotonic
2026-01-14T10:00:55.556413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
851465
 
1.7%
1781352
 
1.6%
1121345
 
1.5%
841178
 
1.4%
801062
 
1.2%
631062
 
1.2%
751056
 
1.2%
921028
 
1.2%
1791008
 
1.2%
1611000
 
1.1%
Other values (130)75415
86.7%
ValueCountFrequency (%)
46580
0.7%
47724
0.8%
48828
1.0%
49351
0.4%
50557
0.6%
51631
0.7%
52682
0.8%
53743
0.9%
54515
0.6%
55336
0.4%
ValueCountFrequency (%)
185936
1.1%
184366
 
0.4%
183417
 
0.5%
182251
 
0.3%
181817
0.9%
180376
 
0.4%
1791008
1.2%
1781352
1.6%
177660
0.8%
176885
1.0%

replacement_cost
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.199909
Minimum9.99
Maximum29.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size679.6 KiB
2026-01-14T10:00:55.593099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9.99
5-th percentile10.99
Q114.99
median20.99
Q325.99
95-th percentile29.99
Maximum29.99
Range20
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.0907494
Coefficient of variation (CV)0.3015236
Kurtosis-1.2239214
Mean20.199909
Median Absolute Deviation (MAD)6
Skewness-0.037070367
Sum1756806.3
Variance37.097228
MonotonicityNot monotonic
2026-01-14T10:00:55.627579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
22.995561
 
6.4%
27.995168
 
5.9%
12.994964
 
5.7%
21.994845
 
5.6%
14.994818
 
5.5%
29.994660
 
5.4%
20.994620
 
5.3%
26.994226
 
4.9%
13.994146
 
4.8%
11.994100
 
4.7%
Other values (11)39863
45.8%
ValueCountFrequency (%)
9.993888
4.5%
10.993482
4.0%
11.994100
4.7%
12.994964
5.7%
13.994146
4.8%
14.994818
5.5%
15.993123
3.6%
16.993557
4.1%
17.993692
4.2%
18.993661
4.2%
ValueCountFrequency (%)
29.994660
5.4%
28.994046
4.7%
27.995168
5.9%
26.994226
4.9%
25.993668
4.2%
24.993383
3.9%
23.993835
4.4%
22.995561
6.4%
21.994845
5.6%
20.994620
5.3%

rating
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size679.6 KiB
PG-13
18850 
PG
18411 
NC-17
17388 
R
17053 
G
15269 

Length

Max length5
Median length2
Mean length2.8783618
Min length1

Characters and Unicode

Total characters250334
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowG
2nd rowG
3rd rowG
4th rowG
5th rowR

Common Values

ValueCountFrequency (%)
PG-1318850
21.7%
PG18411
21.2%
NC-1717388
20.0%
R17053
19.6%
G15269
17.6%

Length

2026-01-14T10:00:55.665806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T10:00:55.694170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
pg-1318850
21.7%
pg18411
21.2%
nc-1717388
20.0%
r17053
19.6%
g15269
17.6%

Most occurring characters

ValueCountFrequency (%)
G52530
21.0%
P37261
14.9%
-36238
14.5%
136238
14.5%
318850
 
7.5%
N17388
 
6.9%
C17388
 
6.9%
717388
 
6.9%
R17053
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter141620
56.6%
Decimal Number72476
29.0%
Dash Punctuation36238
 
14.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G52530
37.1%
P37261
26.3%
N17388
 
12.3%
C17388
 
12.3%
R17053
 
12.0%
Decimal Number
ValueCountFrequency (%)
136238
50.0%
318850
26.0%
717388
24.0%
Dash Punctuation
ValueCountFrequency (%)
-36238
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin141620
56.6%
Common108714
43.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
G52530
37.1%
P37261
26.3%
N17388
 
12.3%
C17388
 
12.3%
R17053
 
12.0%
Common
ValueCountFrequency (%)
-36238
33.3%
136238
33.3%
318850
17.3%
717388
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII250334
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G52530
21.0%
P37261
14.9%
-36238
14.5%
136238
14.5%
318850
 
7.5%
N17388
 
6.9%
C17388
 
6.9%
717388
 
6.9%
R17053
 
6.8%

actor_id
Real number (ℝ)

Distinct200
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.9395
Minimum1
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size679.6 KiB
2026-01-14T10:00:55.734674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q152
median102
Q3149
95-th percentile191
Maximum200
Range199
Interquartile range (IQR)97

Descriptive statistics

Standard deviation56.883288
Coefficient of variation (CV)0.56353845
Kurtosis-1.1741743
Mean100.9395
Median Absolute Deviation (MAD)48
Skewness0.0025358136
Sum8778809
Variance3235.7084
MonotonicityNot monotonic
2026-01-14T10:00:55.779625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107742
 
0.9%
181664
 
0.8%
198663
 
0.8%
144645
 
0.7%
102632
 
0.7%
60608
 
0.7%
150602
 
0.7%
37598
 
0.7%
23593
 
0.7%
90588
 
0.7%
Other values (190)80636
92.7%
ValueCountFrequency (%)
1301
0.3%
2384
0.4%
3308
0.4%
4272
0.3%
5490
0.6%
6273
0.3%
7476
0.5%
8314
0.4%
9376
0.4%
10355
0.4%
ValueCountFrequency (%)
200339
0.4%
199248
 
0.3%
198663
0.8%
197546
0.6%
196450
0.5%
195443
0.5%
194378
0.4%
193438
0.5%
192452
0.5%
191515
0.6%
Distinct128
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size679.6 KiB
2026-01-14T10:00:55.900908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length8
Mean length5.3014798
Min length2

Characters and Unicode

Total characters461075
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFRED
2nd rowALAN
3rd rowBURT
4th rowTHORA
5th rowTOM
ValueCountFrequency (%)
penelope1637
 
1.9%
kenneth1625
 
1.9%
jayne1544
 
1.8%
matthew1496
 
1.7%
julia1414
 
1.6%
groucho1333
 
1.5%
morgan1322
 
1.5%
ed1295
 
1.5%
burt1291
 
1.5%
christian1271
 
1.5%
Other values (118)72743
83.6%
2026-01-14T10:00:56.135442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E64486
14.0%
A52085
 
11.3%
R40638
 
8.8%
N40267
 
8.7%
L29241
 
6.3%
I29081
 
6.3%
O18393
 
4.0%
S18364
 
4.0%
T17941
 
3.9%
C17009
 
3.7%
Other values (14)133570
29.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter461075
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E64486
14.0%
A52085
 
11.3%
R40638
 
8.8%
N40267
 
8.7%
L29241
 
6.3%
I29081
 
6.3%
O18393
 
4.0%
S18364
 
4.0%
T17941
 
3.9%
C17009
 
3.7%
Other values (14)133570
29.0%

Most occurring scripts

ValueCountFrequency (%)
Latin461075
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E64486
14.0%
A52085
 
11.3%
R40638
 
8.8%
N40267
 
8.7%
L29241
 
6.3%
I29081
 
6.3%
O18393
 
4.0%
S18364
 
4.0%
T17941
 
3.9%
C17009
 
3.7%
Other values (14)133570
29.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII461075
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E64486
14.0%
A52085
 
11.3%
R40638
 
8.8%
N40267
 
8.7%
L29241
 
6.3%
I29081
 
6.3%
O18393
 
4.0%
S18364
 
4.0%
T17941
 
3.9%
C17009
 
3.7%
Other values (14)133570
29.0%
Distinct121
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size679.6 KiB
2026-01-14T10:00:56.295853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length9
Mean length6.2449207
Min length3

Characters and Unicode

Total characters543127
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCOSTNER
2nd rowDREYFUSS
3rd rowTEMPLE
4th rowTEMPLE
5th rowMIRANDA
ValueCountFrequency (%)
kilmer2124
 
2.4%
nolte2090
 
2.4%
temple1756
 
2.0%
degeneres1593
 
1.8%
keitel1564
 
1.8%
berry1464
 
1.7%
torn1459
 
1.7%
hoffman1444
 
1.7%
guiness1407
 
1.6%
garland1379
 
1.6%
Other values (111)70691
81.3%
2026-01-14T10:00:56.530267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E67826
12.5%
N44339
 
8.2%
R40636
 
7.5%
A39274
 
7.2%
O39205
 
7.2%
L39012
 
7.2%
I33562
 
6.2%
S31932
 
5.9%
T23352
 
4.3%
D21252
 
3.9%
Other values (17)162737
30.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter542645
99.9%
Dash Punctuation482
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E67826
12.5%
N44339
 
8.2%
R40636
 
7.5%
A39274
 
7.2%
O39205
 
7.2%
L39012
 
7.2%
I33562
 
6.2%
S31932
 
5.9%
T23352
 
4.3%
D21252
 
3.9%
Other values (16)162255
29.9%
Dash Punctuation
ValueCountFrequency (%)
-482
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin542645
99.9%
Common482
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E67826
12.5%
N44339
 
8.2%
R40636
 
7.5%
A39274
 
7.2%
O39205
 
7.2%
L39012
 
7.2%
I33562
 
6.2%
S31932
 
5.9%
T23352
 
4.3%
D21252
 
3.9%
Other values (16)162255
29.9%
Common
ValueCountFrequency (%)
-482
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII543127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E67826
12.5%
N44339
 
8.2%
R40636
 
7.5%
A39274
 
7.2%
O39205
 
7.2%
L39012
 
7.2%
I33562
 
6.2%
S31932
 
5.9%
T23352
 
4.3%
D21252
 
3.9%
Other values (17)162737
30.0%

customer_id
Real number (ℝ)

High correlation 

Distinct599
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean297.3076
Minimum1
Maximum599
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size679.6 KiB
2026-01-14T10:00:56.576516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile29
Q1148
median296
Q3446
95-th percentile568
Maximum599
Range598
Interquartile range (IQR)298

Descriptive statistics

Standard deviation172.18532
Coefficient of variation (CV)0.57914873
Kurtosis-1.1879078
Mean297.3076
Median Absolute Deviation (MAD)149
Skewness0.0073978278
Sum25857139
Variance29647.783
MonotonicityNot monotonic
2026-01-14T10:00:56.984393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
148272
 
0.3%
526251
 
0.3%
197244
 
0.3%
144239
 
0.3%
236225
 
0.3%
257221
 
0.3%
29217
 
0.2%
178217
 
0.2%
75212
 
0.2%
506211
 
0.2%
Other values (589)84662
97.3%
ValueCountFrequency (%)
1156
0.2%
2175
0.2%
3144
0.2%
4121
0.1%
5175
0.2%
6171
0.2%
7191
0.2%
8152
0.2%
9122
0.1%
10123
0.1%
ValueCountFrequency (%)
599102
0.1%
598102
0.1%
597139
0.2%
596154
0.2%
595141
0.2%
594141
0.2%
593129
0.1%
592139
0.2%
591120
0.1%
590129
0.1%

cust_fn
Text

Distinct591
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size679.6 KiB
2026-01-14T10:00:57.148286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length10
Mean length5.6722471
Min length2

Characters and Unicode

Total characters493321
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHARLOTTE
2nd rowCHARLOTTE
3rd rowCHARLOTTE
4th rowCHARLOTTE
5th rowTOMMY
ValueCountFrequency (%)
marion385
 
0.4%
leslie325
 
0.4%
tracy298
 
0.3%
terry282
 
0.3%
willie280
 
0.3%
jamie277
 
0.3%
jessie276
 
0.3%
eleanor272
 
0.3%
kelly272
 
0.3%
karl251
 
0.3%
Other values (581)84053
96.6%
2026-01-14T10:00:57.368683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E62268
12.6%
A60578
12.3%
R46544
 
9.4%
N42296
 
8.6%
I38227
 
7.7%
L36270
 
7.4%
O23170
 
4.7%
T21174
 
4.3%
S19394
 
3.9%
D18697
 
3.8%
Other values (16)124703
25.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter493321
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E62268
12.6%
A60578
12.3%
R46544
 
9.4%
N42296
 
8.6%
I38227
 
7.7%
L36270
 
7.4%
O23170
 
4.7%
T21174
 
4.3%
S19394
 
3.9%
D18697
 
3.8%
Other values (16)124703
25.3%

Most occurring scripts

ValueCountFrequency (%)
Latin493321
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E62268
12.6%
A60578
12.3%
R46544
 
9.4%
N42296
 
8.6%
I38227
 
7.7%
L36270
 
7.4%
O23170
 
4.7%
T21174
 
4.3%
S19394
 
3.9%
D18697
 
3.8%
Other values (16)124703
25.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII493321
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E62268
12.6%
A60578
12.3%
R46544
 
9.4%
N42296
 
8.6%
I38227
 
7.7%
L36270
 
7.4%
O23170
 
4.7%
T21174
 
4.3%
S19394
 
3.9%
D18697
 
3.8%
Other values (16)124703
25.3%

cust_ln
Text

Distinct599
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size679.6 KiB
2026-01-14T10:00:57.494531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length10
Mean length6.1929609
Min length2

Characters and Unicode

Total characters538608
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHUNTER
2nd rowHUNTER
3rd rowHUNTER
4th rowHUNTER
5th rowCOLLAZO
ValueCountFrequency (%)
hunt272
 
0.3%
seal251
 
0.3%
peters244
 
0.3%
shaw239
 
0.3%
dean225
 
0.3%
douglas221
 
0.3%
hernandez217
 
0.2%
snyder217
 
0.2%
sanders212
 
0.2%
seward211
 
0.2%
Other values (589)84662
97.3%
2026-01-14T10:00:57.651999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E57552
 
10.7%
R53721
 
10.0%
A46896
 
8.7%
N42993
 
8.0%
O38448
 
7.1%
L38183
 
7.1%
S35551
 
6.6%
I26602
 
4.9%
T25723
 
4.8%
H19807
 
3.7%
Other values (16)153132
28.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter538608
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E57552
 
10.7%
R53721
 
10.0%
A46896
 
8.7%
N42993
 
8.0%
O38448
 
7.1%
L38183
 
7.1%
S35551
 
6.6%
I26602
 
4.9%
T25723
 
4.8%
H19807
 
3.7%
Other values (16)153132
28.4%

Most occurring scripts

ValueCountFrequency (%)
Latin538608
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E57552
 
10.7%
R53721
 
10.0%
A46896
 
8.7%
N42993
 
8.0%
O38448
 
7.1%
L38183
 
7.1%
S35551
 
6.6%
I26602
 
4.9%
T25723
 
4.8%
H19807
 
3.7%
Other values (16)153132
28.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII538608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E57552
 
10.7%
R53721
 
10.0%
A46896
 
8.7%
N42993
 
8.0%
O38448
 
7.1%
L38183
 
7.1%
S35551
 
6.6%
I26602
 
4.9%
T25723
 
4.8%
H19807
 
3.7%
Other values (16)153132
28.4%

email
Text

Distinct599
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size679.6 KiB
2026-01-14T10:00:57.778628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length40
Median length38
Mean length31.865208
Min length26

Characters and Unicode

Total characters2771349
Distinct characters41
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHARLOTTE.HUNTER@sakilacustomer.org
2nd rowCHARLOTTE.HUNTER@sakilacustomer.org
3rd rowCHARLOTTE.HUNTER@sakilacustomer.org
4th rowCHARLOTTE.HUNTER@sakilacustomer.org
5th rowTOMMY.COLLAZO@sakilacustomer.org
ValueCountFrequency (%)
eleanor.hunt@sakilacustomer.org272
 
0.3%
karl.seal@sakilacustomer.org251
 
0.3%
sue.peters@sakilacustomer.org244
 
0.3%
clara.shaw@sakilacustomer.org239
 
0.3%
marcia.dean@sakilacustomer.org225
 
0.3%
marsha.douglas@sakilacustomer.org221
 
0.3%
angela.hernandez@sakilacustomer.org217
 
0.2%
marion.snyder@sakilacustomer.org217
 
0.2%
tammy.sanders@sakilacustomer.org212
 
0.2%
leslie.seward@sakilacustomer.org211
 
0.2%
Other values (589)84662
97.3%
2026-01-14T10:00:58.028542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r173942
 
6.3%
.173942
 
6.3%
o173942
 
6.3%
s173942
 
6.3%
a173942
 
6.3%
E119820
 
4.3%
A107474
 
3.9%
R100265
 
3.6%
l86971
 
3.1%
g86971
 
3.1%
Other values (31)1400138
50.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1478507
53.3%
Uppercase Letter1031929
37.2%
Other Punctuation260913
 
9.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E119820
11.6%
A107474
 
10.4%
R100265
 
9.7%
N85289
 
8.3%
L74453
 
7.2%
I64829
 
6.3%
O61618
 
6.0%
S54945
 
5.3%
T46897
 
4.5%
C35920
 
3.5%
Other values (16)280419
27.2%
Lowercase Letter
ValueCountFrequency (%)
r173942
11.8%
o173942
11.8%
s173942
11.8%
a173942
11.8%
l86971
 
5.9%
g86971
 
5.9%
e86971
 
5.9%
t86971
 
5.9%
u86971
 
5.9%
c86971
 
5.9%
Other values (3)260913
17.6%
Other Punctuation
ValueCountFrequency (%)
.173942
66.7%
@86971
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin2510436
90.6%
Common260913
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
r173942
 
6.9%
o173942
 
6.9%
s173942
 
6.9%
a173942
 
6.9%
E119820
 
4.8%
A107474
 
4.3%
R100265
 
4.0%
l86971
 
3.5%
g86971
 
3.5%
e86971
 
3.5%
Other values (29)1226196
48.8%
Common
ValueCountFrequency (%)
.173942
66.7%
@86971
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2771349
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r173942
 
6.3%
.173942
 
6.3%
o173942
 
6.3%
s173942
 
6.3%
a173942
 
6.3%
E119820
 
4.3%
A107474
 
3.9%
R100265
 
3.6%
l86971
 
3.1%
g86971
 
3.1%
Other values (31)1400138
50.5%

active
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size679.6 KiB
1
84729 
0
 
2242

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters86971
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
184729
97.4%
02242
 
2.6%

Length

2026-01-14T10:00:58.114056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T10:00:58.138139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
184729
97.4%
02242
 
2.6%

Most occurring characters

ValueCountFrequency (%)
184729
97.4%
02242
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number86971
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
184729
97.4%
02242
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common86971
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
184729
97.4%
02242
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII86971
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
184729
97.4%
02242
 
2.6%

rental_id
Real number (ℝ)

Distinct15821
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7969.3271
Minimum1
Maximum16049
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size679.6 KiB
2026-01-14T10:00:58.166874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile811
Q13983
median7948
Q311918.5
95-th percentile15222.5
Maximum16049
Range16048
Interquartile range (IQR)7935.5

Descriptive statistics

Standard deviation4615.1429
Coefficient of variation (CV)0.57911325
Kurtosis-1.1897433
Mean7969.3271
Median Absolute Deviation (MAD)3968
Skewness0.016729421
Sum6.9310035 × 108
Variance21299544
MonotonicityIncreasing
2026-01-14T10:00:58.214116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1270115
 
< 0.1%
801415
 
< 0.1%
484515
 
< 0.1%
1518715
 
< 0.1%
1420015
 
< 0.1%
522115
 
< 0.1%
279615
 
< 0.1%
593015
 
< 0.1%
744615
 
< 0.1%
715215
 
< 0.1%
Other values (15811)86821
99.8%
ValueCountFrequency (%)
14
< 0.1%
25
< 0.1%
32
 
< 0.1%
44
< 0.1%
58
< 0.1%
64
< 0.1%
76
< 0.1%
84
< 0.1%
93
 
< 0.1%
107
< 0.1%
ValueCountFrequency (%)
160496
< 0.1%
160482
 
< 0.1%
160477
< 0.1%
160465
< 0.1%
160455
< 0.1%
160445
< 0.1%
160437
< 0.1%
160423
 
< 0.1%
160416
< 0.1%
160408
< 0.1%
Distinct15774
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Memory size679.6 KiB
Minimum2005-05-24 22:53:30
Maximum2005-08-23 22:50:12
Invalid dates0
Invalid dates (%)0.0%
2026-01-14T10:00:58.292494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:58.391896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct15796
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size679.6 KiB
Minimum2005-05-25 23:55:21
Maximum2005-09-02 02:35:22
Invalid dates0
Invalid dates (%)0.0%
2026-01-14T10:00:58.480555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:58.528286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

payment_id
Real number (ℝ)

High correlation 

Distinct15821
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8028.9088
Minimum1
Maximum16049
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size679.6 KiB
2026-01-14T10:00:58.570587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile798
Q14038
median8040
Q312028
95-th percentile15217
Maximum16049
Range16048
Interquartile range (IQR)7990

Descriptive statistics

Standard deviation4625.7166
Coefficient of variation (CV)0.57613266
Kurtosis-1.1973239
Mean8028.9088
Median Absolute Deviation (MAD)3994
Skewness-0.0075447733
Sum6.9828223 × 108
Variance21397254
MonotonicityNot monotonic
2026-01-14T10:00:58.614034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1320215
 
< 0.1%
28815
 
< 0.1%
1483915
 
< 0.1%
1246315
 
< 0.1%
945715
 
< 0.1%
228015
 
< 0.1%
566215
 
< 0.1%
685315
 
< 0.1%
1312915
 
< 0.1%
1000415
 
< 0.1%
Other values (15811)86821
99.8%
ValueCountFrequency (%)
14
< 0.1%
25
< 0.1%
31
 
< 0.1%
43
< 0.1%
51
 
< 0.1%
66
< 0.1%
74
< 0.1%
86
< 0.1%
95
< 0.1%
105
< 0.1%
ValueCountFrequency (%)
160494
< 0.1%
160488
< 0.1%
160475
< 0.1%
160464
< 0.1%
160453
 
< 0.1%
160445
< 0.1%
160433
 
< 0.1%
160425
< 0.1%
160419
< 0.1%
160406
< 0.1%

amount
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1929297
Minimum0.99
Maximum11.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size679.6 KiB
2026-01-14T10:00:58.648800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.99
5-th percentile0.99
Q12.99
median3.99
Q34.99
95-th percentile8.99
Maximum11.99
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.3424864
Coefficient of variation (CV)0.55867534
Kurtosis-0.24019559
Mean4.1929297
Median Absolute Deviation (MAD)1
Skewness0.47005036
Sum364663.29
Variance5.4872427
MonotonicityNot monotonic
2026-01-14T10:00:58.675874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4.9920213
23.2%
2.9919666
22.6%
0.9915965
18.4%
5.997181
 
8.3%
3.996340
 
7.3%
6.995929
 
6.8%
7.993654
 
4.2%
1.993490
 
4.0%
8.992633
 
3.0%
9.991302
 
1.5%
Other values (2)598
 
0.7%
ValueCountFrequency (%)
0.9915965
18.4%
1.993490
 
4.0%
2.9919666
22.6%
3.996340
 
7.3%
4.9920213
23.2%
5.997181
 
8.3%
6.995929
 
6.8%
7.993654
 
4.2%
8.992633
 
3.0%
9.991302
 
1.5%
ValueCountFrequency (%)
11.9949
 
0.1%
10.99549
 
0.6%
9.991302
 
1.5%
8.992633
 
3.0%
7.993654
 
4.2%
6.995929
 
6.8%
5.997181
 
8.3%
4.9920213
23.2%
3.996340
 
7.3%
2.9919666
22.6%
Distinct15774
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Memory size679.6 KiB
Minimum2005-05-24 22:53:30
Maximum2005-08-23 22:50:12
Invalid dates0
Invalid dates (%)0.0%
2026-01-14T10:00:58.712001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:58.796662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

inventory_id
Real number (ℝ)

High correlation 

Distinct4567
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2297.3186
Minimum1
Maximum4581
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size679.6 KiB
2026-01-14T10:00:58.880561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile214
Q11160
median2289
Q33428
95-th percentile4368
Maximum4581
Range4580
Interquartile range (IQR)2268

Descriptive statistics

Standard deviation1320.7305
Coefficient of variation (CV)0.57490088
Kurtosis-1.1807221
Mean2297.3186
Median Absolute Deviation (MAD)1132
Skewness-0.0074845396
Sum1.9980009 × 108
Variance1744329
MonotonicityNot monotonic
2026-01-14T10:00:58.924539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112265
 
0.1%
112365
 
0.1%
242460
 
0.1%
15860
 
0.1%
242360
 
0.1%
189960
 
0.1%
409560
 
0.1%
237160
 
0.1%
233760
 
0.1%
296355
 
0.1%
Other values (4557)86366
99.3%
ValueCountFrequency (%)
130
< 0.1%
250
0.1%
320
 
< 0.1%
420
 
< 0.1%
640
< 0.1%
740
< 0.1%
820
 
< 0.1%
94
 
< 0.1%
1012
 
< 0.1%
118
 
< 0.1%
ValueCountFrequency (%)
458115
< 0.1%
45806
 
< 0.1%
457915
< 0.1%
45789
 
< 0.1%
457715
< 0.1%
457612
< 0.1%
457512
< 0.1%
45749
 
< 0.1%
457325
< 0.1%
457220
< 0.1%

store_id
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size679.6 KiB
1
47427 
2
39544 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters86971
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
147427
54.5%
239544
45.5%

Length

2026-01-14T10:00:58.964986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T10:00:58.988563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
147427
54.5%
239544
45.5%

Most occurring characters

ValueCountFrequency (%)
147427
54.5%
239544
45.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number86971
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
147427
54.5%
239544
45.5%

Most occurring scripts

ValueCountFrequency (%)
Common86971
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
147427
54.5%
239544
45.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII86971
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
147427
54.5%
239544
45.5%

staff_id
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size679.6 KiB
1
47427 
2
39544 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters86971
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
147427
54.5%
239544
45.5%

Length

2026-01-14T10:00:59.015961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T10:00:59.039280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
147427
54.5%
239544
45.5%

Most occurring characters

ValueCountFrequency (%)
147427
54.5%
239544
45.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number86971
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
147427
54.5%
239544
45.5%

Most occurring scripts

ValueCountFrequency (%)
Common86971
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
147427
54.5%
239544
45.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII86971
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
147427
54.5%
239544
45.5%

staff_fn
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size679.6 KiB
Mike
47427 
Jon
39544 

Length

Max length4
Median length4
Mean length3.5453197
Min length3

Characters and Unicode

Total characters308340
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMike
2nd rowMike
3rd rowMike
4th rowMike
5th rowMike

Common Values

ValueCountFrequency (%)
Mike47427
54.5%
Jon39544
45.5%

Length

2026-01-14T10:00:59.064933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T10:00:59.085307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mike47427
54.5%
jon39544
45.5%

Most occurring characters

ValueCountFrequency (%)
M47427
15.4%
i47427
15.4%
k47427
15.4%
e47427
15.4%
J39544
12.8%
o39544
12.8%
n39544
12.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter221369
71.8%
Uppercase Letter86971
 
28.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i47427
21.4%
k47427
21.4%
e47427
21.4%
o39544
17.9%
n39544
17.9%
Uppercase Letter
ValueCountFrequency (%)
M47427
54.5%
J39544
45.5%

Most occurring scripts

ValueCountFrequency (%)
Latin308340
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M47427
15.4%
i47427
15.4%
k47427
15.4%
e47427
15.4%
J39544
12.8%
o39544
12.8%
n39544
12.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII308340
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M47427
15.4%
i47427
15.4%
k47427
15.4%
e47427
15.4%
J39544
12.8%
o39544
12.8%
n39544
12.8%

staff_ln
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size679.6 KiB
Hillyer
47427 
Stephens
39544 

Length

Max length8
Median length7
Mean length7.4546803
Min length7

Characters and Unicode

Total characters648341
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHillyer
2nd rowHillyer
3rd rowHillyer
4th rowHillyer
5th rowHillyer

Common Values

ValueCountFrequency (%)
Hillyer47427
54.5%
Stephens39544
45.5%

Length

2026-01-14T10:00:59.112189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T10:00:59.155296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
hillyer47427
54.5%
stephens39544
45.5%

Most occurring characters

ValueCountFrequency (%)
e126515
19.5%
l94854
14.6%
H47427
 
7.3%
i47427
 
7.3%
y47427
 
7.3%
r47427
 
7.3%
S39544
 
6.1%
t39544
 
6.1%
p39544
 
6.1%
h39544
 
6.1%
Other values (2)79088
12.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter561370
86.6%
Uppercase Letter86971
 
13.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e126515
22.5%
l94854
16.9%
i47427
 
8.4%
y47427
 
8.4%
r47427
 
8.4%
t39544
 
7.0%
p39544
 
7.0%
h39544
 
7.0%
n39544
 
7.0%
s39544
 
7.0%
Uppercase Letter
ValueCountFrequency (%)
H47427
54.5%
S39544
45.5%

Most occurring scripts

ValueCountFrequency (%)
Latin648341
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e126515
19.5%
l94854
14.6%
H47427
 
7.3%
i47427
 
7.3%
y47427
 
7.3%
r47427
 
7.3%
S39544
 
6.1%
t39544
 
6.1%
p39544
 
6.1%
h39544
 
6.1%
Other values (2)79088
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII648341
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e126515
19.5%
l94854
14.6%
H47427
 
7.3%
i47427
 
7.3%
y47427
 
7.3%
r47427
 
7.3%
S39544
 
6.1%
t39544
 
6.1%
p39544
 
6.1%
h39544
 
6.1%
Other values (2)79088
12.2%

address_id
Real number (ℝ)

High correlation 

Distinct599
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean302.02316
Minimum5
Maximum605
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size679.6 KiB
2026-01-14T10:00:59.229738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile33
Q1152
median301
Q3451
95-th percentile574
Maximum605
Range600
Interquartile range (IQR)299

Descriptive statistics

Standard deviation172.81561
Coefficient of variation (CV)0.57219324
Kurtosis-1.1881586
Mean302.02316
Median Absolute Deviation (MAD)149
Skewness0.0090934767
Sum26267256
Variance29865.234
MonotonicityNot monotonic
2026-01-14T10:00:59.301362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
152272
 
0.3%
532251
 
0.3%
201244
 
0.3%
148239
 
0.3%
240225
 
0.3%
262221
 
0.3%
33217
 
0.2%
182217
 
0.2%
79212
 
0.2%
511211
 
0.2%
Other values (589)84662
97.3%
ValueCountFrequency (%)
5156
0.2%
6175
0.2%
7144
0.2%
8121
0.1%
9175
0.2%
10171
0.2%
11191
0.2%
12152
0.2%
13122
0.1%
14123
0.1%
ValueCountFrequency (%)
605102
0.1%
604102
0.1%
603139
0.2%
602154
0.2%
601141
0.2%
600141
0.2%
599129
0.1%
598139
0.2%
597120
0.1%
596129
0.1%

address
Text

Distinct599
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size679.6 KiB
2026-01-14T10:00:59.469559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length38
Median length34
Mean length19.466282
Min length9

Characters and Unicode

Total characters1693002
Distinct characters66
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row758 Junan Lane
2nd row758 Junan Lane
3rd row758 Junan Lane
4th row758 Junan Lane
5th row76 Kermanshah Manor
ValueCountFrequency (%)
parkway10989
 
3.8%
manor9773
 
3.4%
avenue8832
 
3.1%
way8767
 
3.1%
lane8635
 
3.0%
street8561
 
3.0%
place8117
 
2.8%
loop7980
 
2.8%
boulevard7724
 
2.7%
drive7593
 
2.7%
Other values (957)198978
69.6%
2026-01-14T10:00:59.714064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
198978
 
11.8%
a176965
 
10.5%
e108940
 
6.4%
o86169
 
5.1%
r82593
 
4.9%
n81151
 
4.8%
166932
 
4.0%
i50997
 
3.0%
l50634
 
3.0%
u48441
 
2.9%
Other values (56)741202
43.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter993631
58.7%
Decimal Number296082
 
17.5%
Space Separator198978
 
11.8%
Uppercase Letter196522
 
11.6%
Dash Punctuation3471
 
0.2%
Open Punctuation2159
 
0.1%
Close Punctuation2159
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a176965
17.8%
e108940
11.0%
o86169
 
8.7%
r82593
 
8.3%
n81151
 
8.2%
i50997
 
5.1%
l50634
 
5.1%
u48441
 
4.9%
t40896
 
4.1%
y29143
 
2.9%
Other values (16)237702
23.9%
Uppercase Letter
ValueCountFrequency (%)
P26280
13.4%
L21774
11.1%
S21644
11.0%
B18284
9.3%
A17157
8.7%
M15102
 
7.7%
D10399
 
5.3%
W9368
 
4.8%
C9033
 
4.6%
T6459
 
3.3%
Other values (16)41022
20.9%
Decimal Number
ValueCountFrequency (%)
166932
22.6%
629227
9.9%
428644
9.7%
927998
9.5%
726263
 
8.9%
825982
 
8.8%
524591
 
8.3%
224226
 
8.2%
323063
 
7.8%
019156
 
6.5%
Space Separator
ValueCountFrequency (%)
198978
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3471
100.0%
Open Punctuation
ValueCountFrequency (%)
(2159
100.0%
Close Punctuation
ValueCountFrequency (%)
)2159
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1190153
70.3%
Common502849
29.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a176965
14.9%
e108940
 
9.2%
o86169
 
7.2%
r82593
 
6.9%
n81151
 
6.8%
i50997
 
4.3%
l50634
 
4.3%
u48441
 
4.1%
t40896
 
3.4%
y29143
 
2.4%
Other values (42)434224
36.5%
Common
ValueCountFrequency (%)
198978
39.6%
166932
 
13.3%
629227
 
5.8%
428644
 
5.7%
927998
 
5.6%
726263
 
5.2%
825982
 
5.2%
524591
 
4.9%
224226
 
4.8%
323063
 
4.6%
Other values (4)26945
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1693002
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
198978
 
11.8%
a176965
 
10.5%
e108940
 
6.4%
o86169
 
5.1%
r82593
 
4.9%
n81151
 
4.8%
166932
 
4.0%
i50997
 
3.0%
l50634
 
3.0%
u48441
 
2.9%
Other values (56)741202
43.8%

postal_code
Real number (ℝ)

Distinct596
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50317.771
Minimum3
Maximum99865
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size679.6 KiB
2026-01-14T10:00:59.770524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4855
Q125220
median50805
Q374750
95-th percentile95509
Maximum99865
Range99862
Interquartile range (IQR)49530

Descriptive statistics

Standard deviation28850.283
Coefficient of variation (CV)0.57336171
Kurtosis-1.1649044
Mean50317.771
Median Absolute Deviation (MAD)24704
Skewness-0.017702468
Sum4.3761869 × 109
Variance8.3233886 × 108
MonotonicityNot monotonic
2026-01-14T10:00:59.815293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52137294
 
0.3%
9668284
 
0.3%
92150272
 
0.3%
22474270
 
0.3%
31342251
 
0.3%
89459244
 
0.3%
30861239
 
0.3%
18727225
 
0.3%
86768221
 
0.3%
65750217
 
0.2%
Other values (586)84454
97.1%
ValueCountFrequency (%)
3116
0.1%
400138
0.2%
504130
0.1%
841101
0.1%
943133
0.2%
966114
0.1%
1027124
0.1%
1079122
0.1%
1195146
0.2%
1545190
0.2%
ValueCountFrequency (%)
99865126
0.1%
99780165
0.2%
99552140
0.2%
99457168
0.2%
99405178
0.2%
99124119
0.1%
98889141
0.2%
98883157
0.2%
98775121
0.1%
98573148
0.2%

city_id
Real number (ℝ)

Distinct597
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean301.5238
Minimum1
Maximum600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size679.6 KiB
2026-01-14T10:00:59.857192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile31
Q1148
median305
Q3452
95-th percentile569
Maximum600
Range599
Interquartile range (IQR)304

Descriptive statistics

Standard deviation173.42494
Coefficient of variation (CV)0.5751617
Kurtosis-1.2093799
Mean301.5238
Median Absolute Deviation (MAD)151
Skewness-0.016446523
Sum26223826
Variance30076.209
MonotonicityNot monotonic
2026-01-14T10:01:00.125438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
442272
 
0.3%
42263
 
0.3%
312258
 
0.3%
101251
 
0.3%
109244
 
0.3%
340239
 
0.3%
527225
 
0.3%
64221
 
0.3%
474217
 
0.2%
456217
 
0.2%
Other values (587)84564
97.2%
ValueCountFrequency (%)
1153
0.2%
2120
0.1%
3175
0.2%
4160
0.2%
5124
0.1%
6123
0.1%
7178
0.2%
8136
0.2%
9164
0.2%
10157
0.2%
ValueCountFrequency (%)
600131
0.2%
599162
0.2%
598157
0.2%
597138
0.2%
596153
0.2%
59581
0.1%
594123
0.1%
593144
0.2%
592139
0.2%
591124
0.1%

city
Text

Distinct597
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size679.6 KiB
2026-01-14T10:01:00.273200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length21
Mean length8.3927286
Min length2

Characters and Unicode

Total characters729924
Distinct characters57
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowguas Lindas de Gois
2nd rowguas Lindas de Gois
3rd rowguas Lindas de Gois
4th rowguas Lindas de Gois
5th rowQomsheh
ValueCountFrequency (%)
de1903
 
1.8%
san946
 
0.9%
la724
 
0.7%
del581
 
0.5%
santa575
 
0.5%
el433
 
0.4%
hill404
 
0.4%
santiago395
 
0.4%
plata353
 
0.3%
felipe344
 
0.3%
Other values (673)99477
93.7%
2026-01-14T10:01:00.437984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a108294
 
14.8%
n51398
 
7.0%
o47215
 
6.5%
i45992
 
6.3%
e42212
 
5.8%
r37947
 
5.2%
u33842
 
4.6%
l30043
 
4.1%
s24579
 
3.4%
t24408
 
3.3%
Other values (47)283994
38.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter598395
82.0%
Uppercase Letter105377
 
14.4%
Space Separator19164
 
2.6%
Dash Punctuation3828
 
0.5%
Open Punctuation1501
 
0.2%
Close Punctuation1501
 
0.2%
Other Punctuation158
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a108294
18.1%
n51398
 
8.6%
o47215
 
7.9%
i45992
 
7.7%
e42212
 
7.1%
r37947
 
6.3%
u33842
 
5.7%
l30043
 
5.0%
s24579
 
4.1%
t24408
 
4.1%
Other values (16)152465
25.5%
Uppercase Letter
ValueCountFrequency (%)
S13137
 
12.5%
B9526
 
9.0%
C7163
 
6.8%
A7133
 
6.8%
P6253
 
5.9%
T6165
 
5.9%
K5864
 
5.6%
M5717
 
5.4%
L5133
 
4.9%
H4290
 
4.1%
Other values (16)34996
33.2%
Space Separator
ValueCountFrequency (%)
19164
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3828
100.0%
Open Punctuation
ValueCountFrequency (%)
(1501
100.0%
Close Punctuation
ValueCountFrequency (%)
)1501
100.0%
Other Punctuation
ValueCountFrequency (%)
/158
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin703772
96.4%
Common26152
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a108294
15.4%
n51398
 
7.3%
o47215
 
6.7%
i45992
 
6.5%
e42212
 
6.0%
r37947
 
5.4%
u33842
 
4.8%
l30043
 
4.3%
s24579
 
3.5%
t24408
 
3.5%
Other values (42)257842
36.6%
Common
ValueCountFrequency (%)
19164
73.3%
-3828
 
14.6%
(1501
 
5.7%
)1501
 
5.7%
/158
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII729924
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a108294
 
14.8%
n51398
 
7.0%
o47215
 
6.5%
i45992
 
6.3%
e42212
 
5.8%
r37947
 
5.2%
u33842
 
4.6%
l30043
 
4.1%
s24579
 
3.4%
t24408
 
3.3%
Other values (47)283994
38.9%

country_id
Real number (ℝ)

Distinct108
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.890297
Minimum1
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size679.6 KiB
2026-01-14T10:01:00.488962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q129
median50
Q380
95-th percentile103
Maximum109
Range108
Interquartile range (IQR)51

Descriptive statistics

Standard deviation30.050282
Coefficient of variation (CV)0.52821454
Kurtosis-1.1593477
Mean56.890297
Median Absolute Deviation (MAD)27
Skewness0.054671729
Sum4947806
Variance903.01946
MonotonicityNot monotonic
2026-01-14T10:01:00.545740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
448502
 
9.8%
237638
 
8.8%
1035363
 
6.2%
504504
 
5.2%
604469
 
5.1%
154000
 
4.6%
803877
 
4.5%
753100
 
3.6%
972077
 
2.4%
452001
 
2.3%
Other values (98)41440
47.6%
ValueCountFrequency (%)
1109
 
0.1%
2443
 
0.5%
3111
 
0.1%
4301
 
0.3%
5195
 
0.2%
61841
2.1%
7149
 
0.2%
9403
 
0.5%
10300
 
0.3%
11129
 
0.1%
ValueCountFrequency (%)
109171
 
0.2%
108328
 
0.4%
107621
 
0.7%
106190
 
0.2%
105892
 
1.0%
104953
 
1.1%
1035363
6.2%
1021188
 
1.4%
101474
 
0.5%
100823
 
0.9%

country
Text

Distinct108
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size679.6 KiB
2026-01-14T10:01:00.684668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length37
Median length29
Mean length8.1399892
Min length4

Characters and Unicode

Total characters707943
Distinct characters54
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrazil
2nd rowBrazil
3rd rowBrazil
4th rowBrazil
5th rowIran
ValueCountFrequency (%)
india8502
 
8.0%
china7638
 
7.2%
united7025
 
6.6%
states5363
 
5.0%
japan4504
 
4.2%
mexico4469
 
4.2%
brazil4000
 
3.8%
russian3877
 
3.6%
federation3877
 
3.6%
philippines3100
 
2.9%
Other values (122)54236
50.9%
2026-01-14T10:01:00.905715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a94530
 
13.4%
i79564
 
11.2%
n71781
 
10.1%
e57603
 
8.1%
t35126
 
5.0%
r30693
 
4.3%
d29861
 
4.2%
o26184
 
3.7%
s24857
 
3.5%
l19770
 
2.8%
Other values (44)237974
33.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter581080
82.1%
Uppercase Letter106019
 
15.0%
Space Separator19620
 
2.8%
Other Punctuation818
 
0.1%
Open Punctuation203
 
< 0.1%
Close Punctuation203
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a94530
16.3%
i79564
13.7%
n71781
12.4%
e57603
9.9%
t35126
 
6.0%
r30693
 
5.3%
d29861
 
5.1%
o26184
 
4.5%
s24857
 
4.3%
l19770
 
3.4%
Other values (16)111111
19.1%
Uppercase Letter
ValueCountFrequency (%)
I13788
13.0%
S11152
10.5%
C10935
10.3%
U8038
 
7.6%
A6615
 
6.2%
M6504
 
6.1%
P6468
 
6.1%
B5737
 
5.4%
R5676
 
5.4%
T5413
 
5.1%
Other values (13)25693
24.2%
Other Punctuation
ValueCountFrequency (%)
,438
53.5%
.380
46.5%
Space Separator
ValueCountFrequency (%)
19620
100.0%
Open Punctuation
ValueCountFrequency (%)
(203
100.0%
Close Punctuation
ValueCountFrequency (%)
)203
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin687099
97.1%
Common20844
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a94530
13.8%
i79564
 
11.6%
n71781
 
10.4%
e57603
 
8.4%
t35126
 
5.1%
r30693
 
4.5%
d29861
 
4.3%
o26184
 
3.8%
s24857
 
3.6%
l19770
 
2.9%
Other values (39)217130
31.6%
Common
ValueCountFrequency (%)
19620
94.1%
,438
 
2.1%
.380
 
1.8%
(203
 
1.0%
)203
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII707943
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a94530
 
13.4%
i79564
 
11.2%
n71781
 
10.1%
e57603
 
8.1%
t35126
 
5.0%
r30693
 
4.3%
d29861
 
4.2%
o26184
 
3.7%
s24857
 
3.5%
l19770
 
2.8%
Other values (44)237974
33.6%

category
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size679.6 KiB
Sports
6861 
Animation
6479 
Action
6204 
Documentary
6139 
Drama
5886 
Other values (11)
55402 

Length

Max length11
Median length9
Mean length6.534339
Min length3

Characters and Unicode

Total characters568298
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFamily
2nd rowFamily
3rd rowFamily
4th rowFamily
5th rowMusic

Common Values

ValueCountFrequency (%)
Sports6861
 
7.9%
Animation6479
 
7.4%
Action6204
 
7.1%
Documentary6139
 
7.1%
Drama5886
 
6.8%
Sci-Fi5695
 
6.5%
Family5544
 
6.4%
Foreign5516
 
6.3%
Children5473
 
6.3%
New5131
 
5.9%
Other values (6)28043
32.2%

Length

2026-01-14T10:01:00.946246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sports6861
 
7.9%
animation6479
 
7.4%
action6204
 
7.1%
documentary6139
 
7.1%
drama5886
 
6.8%
sci-fi5695
 
6.5%
family5544
 
6.4%
foreign5516
 
6.3%
children5473
 
6.3%
new5131
 
5.9%
Other values (6)28043
32.2%

Most occurring characters

ValueCountFrequency (%)
i56567
 
10.0%
r49023
 
8.6%
o45518
 
8.0%
a44068
 
7.8%
n36290
 
6.4%
e35971
 
6.3%
m33159
 
5.8%
s30919
 
5.4%
c27520
 
4.8%
t25683
 
4.5%
Other values (20)183580
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter469937
82.7%
Uppercase Letter92666
 
16.3%
Dash Punctuation5695
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i56567
12.0%
r49023
10.4%
o45518
9.7%
a44068
9.4%
n36290
7.7%
e35971
7.7%
m33159
 
7.1%
s30919
 
6.6%
c27520
 
5.9%
t25683
 
5.5%
Other values (9)85219
18.1%
Uppercase Letter
ValueCountFrequency (%)
F16755
18.1%
C15137
16.3%
A12683
13.7%
S12556
13.5%
D12025
13.0%
N5131
 
5.5%
H4849
 
5.2%
T4601
 
5.0%
G4490
 
4.8%
M4439
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
-5695
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin562603
99.0%
Common5695
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i56567
 
10.1%
r49023
 
8.7%
o45518
 
8.1%
a44068
 
7.8%
n36290
 
6.5%
e35971
 
6.4%
m33159
 
5.9%
s30919
 
5.5%
c27520
 
4.9%
t25683
 
4.6%
Other values (19)177885
31.6%
Common
ValueCountFrequency (%)
-5695
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII568298
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i56567
 
10.0%
r49023
 
8.6%
o45518
 
8.0%
a44068
 
7.8%
n36290
 
6.4%
e35971
 
6.3%
m33159
 
5.8%
s30919
 
5.4%
c27520
 
4.8%
t25683
 
4.5%
Other values (20)183580
32.3%

Interactions

2026-01-14T10:00:53.159845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:44.668176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:45.422895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:46.232602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:46.862205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:47.634310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:48.459991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:49.180587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:49.868994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:50.679380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:51.227029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:51.809477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:52.368085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:53.423135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:44.709857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:45.721422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:46.274447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:46.902465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:47.673303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:48.500978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:49.232219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:49.924380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:50.715790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:51.272411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:51.902718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:52.458331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:53.476048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:44.749051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:45.764610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:46.317077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:46.945364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:47.712567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:48.542943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:49.280681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:49.972021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:50.753108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:51.313685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:51.941156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:52.553091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:53.523708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:44.789095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:45.807927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:46.360944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:46.995316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:47.783306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:48.585311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:49.329817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:50.017069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:50.792743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:51.369569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:51.982382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:52.629829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:53.577149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:44.826744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:45.850795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:46.405877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:47.090047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:47.867900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:48.635385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:49.383708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:50.058215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:50.831062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:51.408591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:52.022083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:52.669541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:53.630363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:44.867667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:45.890984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:46.451700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:47.155493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:47.909251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:48.700016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:49.444473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:50.099627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:50.867675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:51.448345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:52.061546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:52.723952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:53.672777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:44.967924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:45.936362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:46.525265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:47.228258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-14T10:00:49.525515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:50.144419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-14T10:00:53.712044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:45.034144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:45.980761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:46.585104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:47.310049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:48.217313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:48.838848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:49.599035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:50.186884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:50.947231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:51.542934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-14T10:00:46.061583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:46.683055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-14T10:00:48.954935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-14T10:00:50.265993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:51.018832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:51.614302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-14T10:00:46.731071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-14T10:00:49.013844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:49.740509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:50.324497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:51.056254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-14T10:00:52.255778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:52.982124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-14T10:00:45.237396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:46.148205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:46.775723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:47.505017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:48.385520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:49.079815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:49.783615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:50.385847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:51.092926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-14T10:00:52.292078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-14T10:00:53.899891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:45.323695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:46.192010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:46.818980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:47.577583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:48.423241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:49.128205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:49.824425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:50.476938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:51.130377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:51.730883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:52.331753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T10:00:53.084764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-14T10:01:00.990373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
activeactor_idaddress_idamountcategorycity_idcountry_idcustomer_idfilm_idinventory_idlengthpayment_idpostal_coderatingrental_durationrental_idrental_ratereplacement_coststaff_fnstaff_idstaff_lnstore_id
active1.0000.0000.0850.0290.0320.1090.0930.0930.0230.0220.0290.0920.1380.0150.0100.0220.0070.0190.0030.0030.0030.003
actor_id0.0001.0000.001-0.0070.0560.0020.0020.0010.0150.0150.0150.0010.0030.0430.041-0.0030.043-0.0020.0000.0000.0000.000
address_id0.0850.0011.0000.0150.0320.041-0.0421.000-0.004-0.004-0.0151.000-0.0430.0200.028-0.0030.027-0.0230.0750.0750.0750.075
amount0.029-0.0070.0151.0000.0680.0010.0120.0150.0270.0270.0030.015-0.0120.0330.1780.0110.716-0.0310.0180.0180.0180.018
category0.0320.0560.0320.0681.0000.0340.0300.0320.1390.1380.1480.0310.0290.1560.1610.0240.1460.1400.0320.0320.0320.032
city_id0.1090.0020.0410.0010.0341.000-0.0580.0410.0100.010-0.0020.041-0.0700.0200.021-0.0040.026-0.0010.1060.1060.1060.106
country_id0.0930.002-0.0420.0120.030-0.0581.000-0.0420.0170.0170.011-0.0420.0010.0250.022-0.0040.0200.0030.1020.1020.1020.102
customer_id0.0930.0011.0000.0150.0320.041-0.0421.000-0.004-0.004-0.0151.000-0.0430.0190.029-0.0030.027-0.0230.0740.0740.0740.074
film_id0.0230.015-0.0040.0270.1390.0100.017-0.0041.0001.0000.053-0.004-0.0080.1200.112-0.0010.113-0.0400.0330.0330.0330.033
inventory_id0.0220.015-0.0040.0270.1380.0100.017-0.0041.0001.0000.053-0.004-0.0080.1190.112-0.0010.116-0.0400.0320.0320.0320.032
length0.0290.015-0.0150.0030.148-0.0020.011-0.0150.0530.0531.000-0.015-0.0040.1010.0870.0020.1050.0150.0170.0170.0170.017
payment_id0.0920.0011.0000.0150.0310.041-0.0421.000-0.004-0.004-0.0151.000-0.0430.0180.028-0.0010.026-0.0230.0730.0730.0730.073
postal_code0.1380.003-0.043-0.0120.029-0.0700.001-0.043-0.008-0.008-0.004-0.0431.0000.0160.0170.0050.027-0.0020.0910.0910.0910.091
rating0.0150.0430.0200.0330.1560.0200.0250.0190.1200.1190.1010.0180.0161.0000.0960.0190.0390.1120.0160.0160.0160.016
rental_duration0.0100.0410.0280.1780.1610.0210.0220.0290.1120.1120.0870.0280.0170.0961.0000.0210.0720.0830.0060.0060.0060.006
rental_id0.022-0.003-0.0030.0110.024-0.004-0.004-0.003-0.001-0.0010.002-0.0010.0050.0190.0211.0000.0240.0060.0280.0280.0280.028
rental_rate0.0070.0430.0270.7160.1460.0260.0200.0270.1130.1160.1050.0260.0270.0390.0720.0241.0000.1090.0000.0000.0000.000
replacement_cost0.019-0.002-0.023-0.0310.140-0.0010.003-0.023-0.040-0.0400.015-0.023-0.0020.1120.0830.0060.1091.0000.0270.0270.0270.027
staff_fn0.0030.0000.0750.0180.0320.1060.1020.0740.0330.0320.0170.0730.0910.0160.0060.0280.0000.0271.0001.0001.0001.000
staff_id0.0030.0000.0750.0180.0320.1060.1020.0740.0330.0320.0170.0730.0910.0160.0060.0280.0000.0271.0001.0001.0001.000
staff_ln0.0030.0000.0750.0180.0320.1060.1020.0740.0330.0320.0170.0730.0910.0160.0060.0280.0000.0271.0001.0001.0001.000
store_id0.0030.0000.0750.0180.0320.1060.1020.0740.0330.0320.0170.0730.0910.0160.0060.0280.0000.0271.0001.0001.0001.000

Missing values

2026-01-14T10:00:54.018518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-14T10:00:54.251826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

film_idtitledescriptionrental_durationrental_ratelengthreplacement_costratingactor_idactor_fnactor_lncustomer_idcust_fncust_lnemailactiverental_idrental_datereturn_datepayment_idamountpayment_dateinventory_idstore_idstaff_idstaff_fnstaff_lnaddress_idaddresspostal_codecity_idcitycountry_idcountrycategory
080BLANKET BEVERLYA Emotional Documentary of a Student And a Girl who must Build a Boat in Nigeria72.9914821.99G16FREDCOSTNER130CHARLOTTEHUNTERCHARLOTTE.HUNTER@sakilacustomer.org112005-05-24 22:53:30.0002005-05-26 22:04:30.00035042.992005-05-24 22:53:30.00036711MikeHillyer134758 Junan Lane82639190guas Lindas de Gois15BrazilFamily
180BLANKET BEVERLYA Emotional Documentary of a Student And a Girl who must Build a Boat in Nigeria72.9914821.99G173ALANDREYFUSS130CHARLOTTEHUNTERCHARLOTTE.HUNTER@sakilacustomer.org112005-05-24 22:53:30.0002005-05-26 22:04:30.00035042.992005-05-24 22:53:30.00036711MikeHillyer134758 Junan Lane82639190guas Lindas de Gois15BrazilFamily
280BLANKET BEVERLYA Emotional Documentary of a Student And a Girl who must Build a Boat in Nigeria72.9914821.99G193BURTTEMPLE130CHARLOTTEHUNTERCHARLOTTE.HUNTER@sakilacustomer.org112005-05-24 22:53:30.0002005-05-26 22:04:30.00035042.992005-05-24 22:53:30.00036711MikeHillyer134758 Junan Lane82639190guas Lindas de Gois15BrazilFamily
380BLANKET BEVERLYA Emotional Documentary of a Student And a Girl who must Build a Boat in Nigeria72.9914821.99G200THORATEMPLE130CHARLOTTEHUNTERCHARLOTTE.HUNTER@sakilacustomer.org112005-05-24 22:53:30.0002005-05-26 22:04:30.00035042.992005-05-24 22:53:30.00036711MikeHillyer134758 Junan Lane82639190guas Lindas de Gois15BrazilFamily
4333FREAKY POCUSA Fast-Paced Documentary of a Pastry Chef And a Crocodile who must Chase a Squirrel in The Gulf of Mexico72.9912616.99R42TOMMIRANDA459TOMMYCOLLAZOTOMMY.COLLAZO@sakilacustomer.org122005-05-24 22:54:33.0002005-05-28 19:40:33.000123772.992005-05-24 22:54:33.000152511MikeHillyer46476 Kermanshah Manor23343423Qomsheh46IranMusic
5333FREAKY POCUSA Fast-Paced Documentary of a Pastry Chef And a Crocodile who must Chase a Squirrel in The Gulf of Mexico72.9912616.99R103MATTHEWLEIGH459TOMMYCOLLAZOTOMMY.COLLAZO@sakilacustomer.org122005-05-24 22:54:33.0002005-05-28 19:40:33.000123772.992005-05-24 22:54:33.000152511MikeHillyer46476 Kermanshah Manor23343423Qomsheh46IranMusic
6333FREAKY POCUSA Fast-Paced Documentary of a Pastry Chef And a Crocodile who must Chase a Squirrel in The Gulf of Mexico72.9912616.99R105SIDNEYCROWE459TOMMYCOLLAZOTOMMY.COLLAZO@sakilacustomer.org122005-05-24 22:54:33.0002005-05-28 19:40:33.000123772.992005-05-24 22:54:33.000152511MikeHillyer46476 Kermanshah Manor23343423Qomsheh46IranMusic
7333FREAKY POCUSA Fast-Paced Documentary of a Pastry Chef And a Crocodile who must Chase a Squirrel in The Gulf of Mexico72.9912616.99R127KEVINGARLAND459TOMMYCOLLAZOTOMMY.COLLAZO@sakilacustomer.org122005-05-24 22:54:33.0002005-05-28 19:40:33.000123772.992005-05-24 22:54:33.000152511MikeHillyer46476 Kermanshah Manor23343423Qomsheh46IranMusic
8333FREAKY POCUSA Fast-Paced Documentary of a Pastry Chef And a Crocodile who must Chase a Squirrel in The Gulf of Mexico72.9912616.99R147FAYWINSLET459TOMMYCOLLAZOTOMMY.COLLAZO@sakilacustomer.org122005-05-24 22:54:33.0002005-05-28 19:40:33.000123772.992005-05-24 22:54:33.000152511MikeHillyer46476 Kermanshah Manor23343423Qomsheh46IranMusic
9373GRADUATE LORDA Lacklusture Epistle of a Girl And a A Shark who must Meet a Mad Scientist in Ancient China72.9915614.99G139EWANGOODING408MANUELMURRELLMANUEL.MURRELL@sakilacustomer.org132005-05-24 23:03:39.0002005-06-01 22:12:39.000110323.992005-05-24 23:03:39.000171111MikeHillyer413692 Amroha Drive35575230Jaffna88Sri LankaChildren
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86964439HUNCHBACK IMPOSSIBLEA Touching Yarn of a Frisbee And a Dentist who must Fight a Composer in Ancient Japan44.9915128.99PG-13175WILLIAMHACKMAN103GLADYSHAMILTONGLADYS.HAMILTON@sakilacustomer.org1160482005-08-23 22:43:07.0002005-08-31 21:33:07.00027998.992005-08-23 22:43:07.000201911MikeHillyer1071177 Jelets Way3305220Ilorin69NigeriaDrama
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86966585MOB DUFFELA Unbelieveable Documentary of a Frisbee And a Boat who must Meet a Boy in The Canadian Rockies40.9910525.99G31SISSYSOBIESKI393PHILIPCAUSEYPHILIP.CAUSEY@sakilacustomer.org1160492005-08-23 22:50:12.0002005-08-30 01:01:12.000106713.992005-08-23 22:50:12.000266611MikeHillyer398954 Lapu-Lapu Way8816278Korolev80Russian FederationDrama
86967585MOB DUFFELA Unbelieveable Documentary of a Frisbee And a Boat who must Meet a Boy in The Canadian Rockies40.9910525.99G32TIMHACKMAN393PHILIPCAUSEYPHILIP.CAUSEY@sakilacustomer.org1160492005-08-23 22:50:12.0002005-08-30 01:01:12.000106713.992005-08-23 22:50:12.000266611MikeHillyer398954 Lapu-Lapu Way8816278Korolev80Russian FederationDrama
86968585MOB DUFFELA Unbelieveable Documentary of a Frisbee And a Boat who must Meet a Boy in The Canadian Rockies40.9910525.99G61CHRISTIANNEESON393PHILIPCAUSEYPHILIP.CAUSEY@sakilacustomer.org1160492005-08-23 22:50:12.0002005-08-30 01:01:12.000106713.992005-08-23 22:50:12.000266611MikeHillyer398954 Lapu-Lapu Way8816278Korolev80Russian FederationDrama
86969585MOB DUFFELA Unbelieveable Documentary of a Frisbee And a Boat who must Meet a Boy in The Canadian Rockies40.9910525.99G103MATTHEWLEIGH393PHILIPCAUSEYPHILIP.CAUSEY@sakilacustomer.org1160492005-08-23 22:50:12.0002005-08-30 01:01:12.000106713.992005-08-23 22:50:12.000266611MikeHillyer398954 Lapu-Lapu Way8816278Korolev80Russian FederationDrama
86970585MOB DUFFELA Unbelieveable Documentary of a Frisbee And a Boat who must Meet a Boy in The Canadian Rockies40.9910525.99G106GROUCHODUNST393PHILIPCAUSEYPHILIP.CAUSEY@sakilacustomer.org1160492005-08-23 22:50:12.0002005-08-30 01:01:12.000106713.992005-08-23 22:50:12.000266611MikeHillyer398954 Lapu-Lapu Way8816278Korolev80Russian FederationDrama